AI Native Developer, hosted by Guy Podjarny and Simon Maple. Interviews looking at how Software Developers are using AI. Released as Audio and Video.
Episodes
16 Jun 2026
Agentic AI systems face critical security risks from overconfidence, prompt-injection vulnerabilities, bypassable guardrails, and performance-driven development, requiring foundational security measures, developer education, and intent-based design to bridge readiness gaps and ensure safe innovation.
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9 Jun 2026
The text explores Codex's integration via Chrome DevTools and TypeScript daemons, agentic development's emphasis on autonomous workflows and trustworthiness, harness engineering's structured tool integration, code QA with automation and feedback loops, shifts in code reviews toward strategy, AI agents as onboarding tools, persistent specs over code, balancing specification precision with adaptability, computational costs of token-heavy processes, and adapting team dynamics to agent-centric workflows.
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2 Jun 2026
AI integration in software development faces challenges like limited agent management (1-2 per developer), lower acceptance of AI-generated code (60% merge rate vs. 80% for human), scalability barriers, and the need for improved observability, workflow alignment, and strategic business integration to balance productivity gains with quality and security.
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26 May 2026
The text addresses security challenges in AI and agentic systems, emphasizing unintended risks like reward-seeking behaviors, the need for developer-centric security strategies, novel attack vectors, frameworks adopting agentic principles, and proposed solutions such as the "AI Bill of Materials" alongside risks like data leakage and governance challenges.
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19 May 2026
Agentic systems introduce heightened security risks through text-based interactions enabling malicious intent encoding, sensitive data access, untrusted inputs, and external system communication, requiring mitigation via SCA, restricted agent access, dynamic analysis, and balancing security with productivity through transparency and adapted security frameworks.
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12 May 2026
AI in software development requires balancing its speed and automation with human accountability for safety and ethics, emphasizing rigorous quality practices, community-driven adoption, critical thinking, and oversight to ensure AI complements rather than replaces human judgment.
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5 May 2026
Integrating AI into enterprises via HTTP calls and existing infrastructure requires balancing language agnosticism, deterministic frameworks like GOAT, Java/Kotlin over Python for reliability, and prioritizing explainability, human oversight, and alignment with business logic over overreliance on AI for simple tasks.
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28 Apr 2026
The text examines challenges in integrating AI into software workflows, highlights AI-native practices like Stripe's Minions automating code tasks, emphasizes balancing human oversight with automation, and explores future trends in agent-native engineering, specialized models, open-source tools, and ethical considerations in AI-driven development.
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21 Apr 2026
Software developers must adapt to AI's complexity by bridging tools with real-world applications, mastering context engineering, shifting toward high-level design and system architecture, and navigating fragmented ecosystems as AGI emerges as an integrated system rather than a standalone model.
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14 Apr 2026
AI in software development shifts from code-centric practices to context- and specification-driven approaches, with humans prioritizing decision-making and oversight while AI handles implementation, but challenges like non-human-readable code, alignment with team practices, and contextual accuracy remain critical.
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